What Happened In 1971?

1 Introduction

---

After more than two decades of economic growth and relative progress, the beginning of the 70s decade signaled an inauspicious reversal: after a sustained period of rising budget deficits and high interest rate following the Johnson administration, Nixon’s economic team started a massive upheaval in American, and by extension, global economic policy. This era of upheaval, otherwise inconspicuous to those outside of macroeconomic and political economy circles, would come to be known as the Nixon shock: the post-war economic system set up around the US as the world’s primary gold reserve and by extension, around the US Dollar as the world’s reserve currency was rapidly undone. The world after 1971 was poised to usher in an era radically different from its predecessor.

This demonstration’s purpose is to showcase the year 1971 and the following years as a watershed moment, both for domestic economy of the US and for the global economic system. Through RStudio’s data aggregation, manipulation, and visualisation suites and using publicly available economic data, I will be examining several key performance indices that might help us glean some insight into the post-Bretton Woods world order.

It is important to note that this is not a policy or economic paper, nor will it attempt to delve deeply into the macroeconomic theory behind the Nixon shock or other developments in the 1970s. Such an analysis would fall beyond my expertise as a data visualisation enthusiast and political economy student. Whether the Shock was an unnecessary misstep by the Nixon administration, or an imperfect inevitability is decidedly beyond the scope of this demonstration, which is intended only to be a showcase for informative purposes.

2 Setting up the R environment

---

This section briefly goes over the technical aspects of setting up R for creating the visualisations. The content here is chiefly for fellow R learners and enthusiasts. If you’re more interested in the graphs, feel free to skip this section. Otherwise click here.

2.1 Packages

For the graphs in this demonstration, I will be using the tidyverse suite of packages for RStudio as the main toolset for data wrangling, cleaning, and visualisation through ggplot2. I will also be using lubridate for datetime manipulation, stringr for string manipulation, fredr for direct data fetching through the Federal Reserve Economic Data site provided by the Federal Reserve’s API. For aesthetic customisation of the document, I use patchwork and extrafont.

library(tidyverse)   # RStudio's suite of extended tools.
library(lubridate)   # For datetime manipulation
library(extrafont)   # For custom fonts.
library(fredr)       # For direct data fetching through FRED's API.
library(patchwork)   # For plot tiling.
library(kableExtra)  # For tables

2.2 API Key

Should you the reader wish to replicate the plots in this demonstration, keep in mind that the fredr package connects through FRED’s API, and thus requires an API key to be set. Here is a sample for the syntax to set an API key, but for your personal use you will need to sign up for a free account with FRED, and set your own API key.

fredr_set_key("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")

2.3 Colour palette

I will also set a custom colour scale for use throughout different visualisations, for the sake of design continuity. The palettes are defined here, and displayed below. The colours and general design are inspired by vintage book covers by Pelican Books (and others in the same vein) from the 1970s.

palette_prim  <- c("#ECA400", 
                   "#006992", 
                   "#b33951")
palette_2nd   <- c("#FFD470", 
                   "#99E2FF",
                   "#DD92A1")
palette_tert  <- c("#FBF6EF",
                   "#0B0A07")
palette_grid  <- c("#768749")

scales::show_col(palette_prim %>% 
                   append(palette_2nd) %>% 
                   append(palette_grid) %>% 
                   append(palette_tert),
                 border = NA)

2.4 Custom ggplot2 theme

To avoid repetition in constructing the plots, I predefine a custom theme based on ggplot2’s theme_minimal() here.

2.4.1 Theme Code

theme_pelican70 <- function () { 
  theme_minimal(base_size=12.5, base_family="Montserrat Light") %+replace%
    theme(
      panel.background      = element_rect(fill = palette_tert[1], colour=NA),
      plot.background       = element_rect(fill = palette_tert[1], colour=NA),
      
      legend.background     = element_rect(fill = "transparent", colour=NA),
      legend.key            = element_rect(fill = "transparent", colour=NA),
      
      plot.margin           = unit(c(t=1,r=1,b=1,l=1),"cm"),
      
      plot.title            = element_text(family = "Newslab Bold",
                                           color = palette_tert[2],
                                           margin = margin(t = 2,
                                                           b = 8,
                                                           unit = "pt")),
      plot.subtitle         = element_text(family = "Newslab",
                                           color = palette_tert[2],
                                           margin = margin(t = 2,
                                                           b = 12,
                                                           unit = "pt")),
      axis.text.y           = element_text(angle = 90,
                                           color=palette_tert[2]),
      axis.text.x           = element_text(color=palette_tert[2]),
      axis.title.x          = element_text(color=palette_tert[2],
                                           face="bold.italic",
                                           margin = margin(t = 2,
                                                           b = 2,
                                                           unit = "pt")),
      axis.title.y          = element_text(color=palette_tert[2],
                                           angle = 90,
                                           face="bold.italic",
                                           margin = margin(t = 2,
                                                           b = 2,
                                                           unit = "pt")),
      strip.text.x          = element_blank(),
      
      panel.grid.major.x    = element_line(linetype = "dotted",
                                           colour = palette_grid),
      panel.grid.major.y    = element_line(linetype = "dotted",
                                           colour = palette_grid),
      panel.grid.minor.x    = element_blank(),
      panel.grid.minor.y    = element_blank(),
      
      plot.caption          = element_text(color=palette_tert[2],
                                           face="italic",
                                           size=7,
                                           hjust = 1,
                                           vjust = 1),
      plot.caption.position = "plot"
    )
}

2.4.2 Plot Example

ggplot(data=mtcars,
       aes(as.factor(gear),mpg,fill=as.factor(gear)))+
  geom_boxplot(col=palette_tert[2])+
  geom_jitter()+
  scale_fill_manual(values = palette_prim)+
  theme_pelican70()+
  labs(title="Example with mtcars dataset: Mileage by gear number",
       x = "Gears",y="Miles per Gallon", fill = "Gears")

3 Domestic Decoupling of the US Economy

---

This section focuses chiefly on how most of the economic growth post-71 in the US has been decoupled from the realities of the average American worker.

The shared prosperity following the end of WW2 would have one believe in “a rising tide lifts all boats” - but this seemed to have stopped being true after 1971, especially for the middle class and working class in the US. In this section, we examine the various ways in which the common American worker has not shared in the continued economic growth since what I call the great decoupling that took place around 1971.

3.1 Productivity vs. Wages

This first plot is probably the most well known: as much as labour productivity has continued to rise, since the 70s, wages have stagnated when adjusted for inflation. Using data from the Economic Policy Institute, here I recreate the plot of relative percentage rise in productivity for all workers and of hourly wage across the US.

Here, we can see clearly that in the few years after 1971, the general rate of increase for wages fell precipitously while productivity continues its previous trajectory. The growing economy had left the worker behind.

3.1.1 Plot

3.1.2 Code

prod_wage <- read_csv("data/productivity_wage.csv") %>%
  mutate_all(funs(str_replace(., "%", ""))) %>%
  mutate_all(funs(str_squish(.))) %>%
  transmute(
    year = as.numeric(Year),
    wage48 = (as.numeric(`Hourly compensation`)+100)/100,
    prod48 = (as.numeric(`Net productivity`)+100)/100
  )

w71 <- prod_wage[prod_wage$year==1971,]$wage48
p71 <- prod_wage[prod_wage$year==1971,]$prod48

prod_wage71 <- prod_wage %>% 
  transmute(
    year = year,
    wage71 = as.numeric(format(round(wage48/w71, 4), nsmall = 4)),
    prod71 = as.numeric(format(round(prod48/p71, 4), nsmall = 4))
  ) %>% 
  filter(year > 1971)

plot_prod_wage <- prod_wage %>%
  pivot_longer(cols = -year,
               names_to = "var",
               values_to = "y") %>%
  mutate(y = (y)) %>%
  ggplot(aes(
    x = year,
    y = y,
    col = var,
    group = var,
    text = paste(sep = "",
                 "Year: ",year,
                 "\n% of 1948: ",y*100,"%")
  )) +
  geom_line(size = 1.25) +
  
  theme_pelican70() +
  scale_color_manual(values = palette_prim) +
  
  scale_x_continuous(breaks = seq(1948, 2018, 8)) +
  scale_y_continuous(labels = scales::percent,
                     breaks = seq(1, 3, 1)) +
  xlim(1945, 2026) +
  
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$wage48 %>% last()) - .5,
    label = "Hourly Wage \nsince '71",
    fontface = 1,
    col = palette_prim[2]
  ) +
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$prod48 %>% last()) - .5,
    label = "Productivity \nsince '71",
    fontface = 1,
    col = palette_prim[1]
  ) +
  
  annotate(
    "point",
    size = 3,
    x = max(prod_wage$year),
    y = (prod_wage$prod48 %>% last()),
    col = palette_prim[1]
  ) +
  annotate(
    "point",
    size = 3,
    x = max(prod_wage$year),
    y = (prod_wage$wage48 %>% last()),
    col = palette_prim[2]
  ) +
  
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$prod48 %>% last()) - .20,
    label = paste("+",
                  ((
                    prod_wage71$prod71 %>% last()
                  ) - 1) * 100,
                  "%",
                  sep = ""),
    fontface = 2,
    size = 5,
    col = palette_prim[1]
  ) +
  annotate(
    "text",
    x = max(prod_wage$year),
    y = (prod_wage$wage48 %>% last()) - .20,
    label = paste("+",
                  ((
                    prod_wage71$wage71 %>% last()
                  ) - 1) * 100,
                  "%",
                  sep = ""),
    fontface = 2,
    size = 5,
    col = palette_prim[2]
  ) +
  
  annotate(
    "segment",
    x = 1971,
    xend = 1971,
    y = .95,
    yend = 3.35,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  ) +
  annotate(
    "text",
    x = 1971,
    y = 3.50,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  labs(
    title = "Figure 1: Productivity and hourly wage rise from 1948-2018",
    subtitle = "",
    x = "Year",
    y = "% of 1948 value\n",
    caption = "Data Source: Economic Policy Institute, BLS"
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_prod_wage,
       filename = "plots/plot_prod_wage.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.1.3 Data

Year	Hourly Wage	Labour Productivity
1948	1.0000	1.0000
1949	1.0624	1.0155
1950	1.1046	1.0934
1951	1.1174	1.1224
1952	1.1502	1.1549
1953	1.2082	1.1941
1954	1.2348	1.2144
1955	1.2870	1.2638
1956	1.3389	1.2659
1957	1.3708	1.3004
1958	1.3808	1.3272
1959	1.4246	1.3763
1960	1.4538	1.4006
1961	1.4784	1.4437
1962	1.5232	1.4980
1963	1.5486	1.5503
1964	1.5832	1.5994
1965	1.6227	1.6492
1966	1.6470	1.6995
1967	1.6668	1.7198
1968	1.7105	1.7713
1969	1.7439	1.7785
1970	1.7681	1.8035
1971	1.8166	1.8710
1972	1.9134	1.9220
1973	1.9096	1.9696
1974	1.8705	1.9383
1975	1.8686	1.9811
1976	1.8935	2.0359
1977	1.9282	2.0605
1978	1.9566	2.0827
1979	1.9325	2.0811
1980	1.8805	2.0677
1981	1.8736	2.1050
1982	1.8770	2.0837
1983	1.8849	2.1451
1984	1.8703	2.2021
1985	1.8618	2.2365
1986	1.8725	2.2828
1987	1.8467	2.2880
1988	1.8402	2.3201
1989	1.8393	2.3412
1990	1.8237	2.3695
1991	1.8202	2.3850
1992	1.8320	2.4748
1993	1.8346	2.4851
1994	1.8389	2.5054
1995	1.8276	2.5159
1996	1.8287	2.5624
1997	1.8487	2.6072
1998	1.8927	2.6621
1999	1.9198	2.7346
2000	1.9296	2.7947
2001	1.9560	2.8371
2002	1.9949	2.9150
2003	2.0158	3.0122
2004	2.0056	3.0929
2005	1.9973	3.1529
2006	1.9988	3.1761
2007	2.0145	3.1978
2008	2.0139	3.2139
2009	2.0930	3.2875
2010	2.1100	3.3823
2011	2.0847	3.3821
2012	2.0650	3.3957
2013	2.0840	3.4096
2014	2.0908	3.4291
2015	2.1241	3.4575
2016	2.1439	3.4634
2017	2.1467	3.4978
2018	2.1562	3.5290

3.2 GDP per Capita vs. Federal Minimum Wage

To further demonstrate that prosperity in the economy at large has not trickled down to the working class, this plot shows that while GDP per capita has risen greatly since ’71, the inflation-adjusted value of the federal minimum wage level has actually fallen sine 1971.

3.2.1 Plot

3.2.2 Code

minwage_gdpcapita <- fredr(
  series_id = "FEDMINNFRWG",
  observation_start = as.Date("1960-01-01"),
  observation_end = as.Date("2019-01-02")
) %>% rbind(
  fredr(
    series_id = "USACPIALLMINMEI",
    observation_start = as.Date("1960-01-01"),
    observation_end = as.Date("2019-01-02")
  )
) %>% 
  pivot_wider(
    id_cols = date,
    names_from = series_id,
    values_from = value
  ) %>% 
  mutate(
    adj_minwage = FEDMINNFRWG/USACPIALLMINMEI*100
  ) %>% 
  filter(month(date) == 1) %>% 
  select(date,adj_minwage) %>% 
  pivot_longer(
    cols = -date,
    names_to = "series_id",
    values_to = "value"
  ) %>% 
  rbind(
    fredr(
      series_id = "A939RX0Q048SBEA",
      observation_start = as.Date("1960-01-01"),
      observation_end = as.Date("2019-01-02")
    )
  ) %>% 
  filter(month(date) == 1) %>% 
  mutate(date = year(date)) %>% 
  group_by(series_id) %>% 
  mutate(
    val_adj = value/first(value)
  ) 

last_val_gdp <- last(minwage_gdpcapita[minwage_gdpcapita$series_id == "A939RX0Q048SBEA",]$val_adj)
last_val_fmw <- last(minwage_gdpcapita[minwage_gdpcapita$series_id == "adj_minwage",]$val_adj)

text_gdp <- paste("+",
                  as.numeric(format(round((last_val_gdp-1)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")
text_fmw <- paste("-",
                  as.numeric(format(round((1-last_val_fmw)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")

plot_minwage_gdppc <- minwage_gdpcapita %>% 
  ggplot(
    aes(
      x = date,
      y = val_adj,
      col = series_id,
      text = paste(sep = "",
                   "Year: ",date,
                   "% of 1960: ",val_adj)
    )
  ) + 
  geom_line(size=1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
    
  xlim(1960,2032)+
  ylim(1,3.6)+
  
  scale_color_manual(values=palette_prim)+
  scale_y_continuous(labels = scales::percent,
                     breaks = seq(1, 3, 1))+
  theme_pelican70()+
  labs(
    title = "Real GDP per capita vs. Federal minimum wage rise 1960-2019",
    subtitle = "",
    x = "Year",
    y = "% of 1960 value \n",
    caption = "Data Source: FRED"
  ) +

  annotate("point",
           x = 2019,
           y = last_val_gdp,
           col = palette_prim[1],
           size=3)+
  annotate("point",
           x = 2019,
           y = last_val_fmw,
           col = palette_prim[2],
           size=3)+
  
  annotate("text",
           x = 2024,
           y = last_val_gdp+.1,
           label = text_gdp,
           col = palette_prim[1],
           fontface = 2,
           size = 5
  )+
  annotate("text",
           x = 2024,
           y = last_val_fmw+.5,
           label = text_fmw,
           col = palette_prim[2],
           fontface = 2,
           size = 5
  )+
  
  annotate("text",
           x = 2024,
           y = last_val_gdp-.1,
           label = "\n GDP/capita \nsince '71",
           col = palette_prim[1],
           size = 4
  )+
  annotate("text",
           x = 2024,
           y = last_val_fmw+.2,
           label = "\n Adj. Federal \nminimum wage \nsince '71",
           col = palette_prim[2],
           size = 4
  )+
  annotate(
    "text",
    x = 1969,
    y = 3.25,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_minwage_gdppc,
       filename = "plots/plot_minwage_gdppc.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.2.3 Data

Year	Adj. Federal Min. Wage	Adj. GDP per capita
1960	8.089317	18268
1961	7.953591	17816
1962	9.085652	18863
1963	8.966104	19257
1964	9.588066	20169
1965	9.495873	20997
1966	9.316706	22510
1967	9.005205	22911
1968	9.730903	23551
1969	10.652449	24365
1970	10.032466	24189
1971	9.528322	24520
1972	9.226939	25083
1973	8.902047	26718
1974	8.137923	26643
1975	9.553468	25789
1976	9.804660	27101
1977	9.318617	27706
1978	10.049521	28549
1979	10.063679	30077
1980	9.444122	30154
1981	9.126517	30316
1982	8.420010	29365
1983	8.118680	29511
1984	7.792021	31762
1985	7.526132	32920
1986	7.244589	33972
1987	7.140350	34585
1988	6.862636	35728
1989	6.556622	36929
1990	6.232394	37593
1991	6.691416	36746
1992	7.294151	37304
1993	7.063971	38029
1994	6.890029	38852
1995	6.702077	39729
1996	6.524108	40292
1997	7.076246	41532
1998	7.553450	43035
1999	7.429322	44600
2000	7.231265	45944
2001	6.971088	46531
2002	6.892363	46690
2003	6.717873	47078
2004	6.590916	48663
2005	6.400826	50081
2006	6.155510	51277
2007	6.030341	51540
2008	6.568834	51637
2009	7.352654	49491
2010	7.930209	49903
2011	7.802878	50495
2012	7.581114	51468
2013	7.462104	51921
2014	7.346113	52293
2015	7.352682	54071
2016	7.253091	54640
2017	7.076183	55401
2018	6.932642	56785
2019	6.826744	57789

3.3 Corporate Profits vs. Median Household Income

An even more dramatic rise can be seen in corporate profits since: aggregate corporate profits after tax has skyrocketed sixfold, but median household income has only doubled in the same time period. Both time series here are adjusted for inflation with a CPI deflator (2018 dollars). Granted, the trajectory for corporate profits is a lot more choppy (especially around the 2008 Global Financial Crisis), even in that era corporate profit still outstrips median household income by more than twice as much.

3.3.1 Plot

3.3.2 Code

corp_medincome <- fredr(
  series_id = "CP",
  observation_start = as.Date("1960-01-01"),
  observation_end = as.Date("2019-01-02")
) %>% rbind(
  fredr(
    series_id = "USACPIALLMINMEI",
    observation_start = as.Date("1960-01-01"),
    observation_end = as.Date("2019-01-02")
  )
) %>% 
  pivot_wider(
    id_cols = date,
    names_from = series_id,
    values_from = value
  ) %>% 
  mutate(
    adj = CP/USACPIALLMINMEI*100
  ) %>% 
  filter(month(date) == 1) %>% 
  select(date,adj) %>% 
  pivot_longer(
    cols = -date,
    names_to = "series_id",
    values_to = "value"
  ) %>% 
  rbind(
    fredr(
      series_id = "MEFAINUSA672N",
      observation_start = as.Date("1960-01-01"),
      observation_end = as.Date("2019-01-02")
    )
  ) %>% 
  filter(month(date) == 1) %>% 
  mutate(date = year(date)) %>% 
  group_by(series_id) %>% 
  mutate(
    val_adj = value/first(value)
  )


last_val_crp <- last(corp_medincome[corp_medincome$series_id == "adj",]$val_adj)
last_val_mhi <- last(corp_medincome[corp_medincome$series_id == "MEFAINUSA672N",]$val_adj)

text_crp <- paste("+",
                  as.numeric(format(round((last_val_crp-1)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")
text_mhi <- paste("+",
                  as.numeric(format(round((last_val_mhi-1)*100, 2), nsmall = 2)),
                  "%",
                  sep = "")

plot_corp_medincome <- corp_medincome %>% 
  ggplot(
    aes(
      x = date,
      y = val_adj,
      col = series_id
    )
  ) + 
  geom_line(size=1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
    
  xlim(1960,2032)+
  ylim(1,3.6)+
  
  scale_color_manual(values=palette_prim)+
  scale_y_continuous(labels = scales::percent)+
  theme_pelican70()+
  labs(
    title = "Adjusted corporate profits vs. median household income 1960-2019",
    subtitle = "",
    x = "Year",
    y = "% of 1960 value \n",
    caption = "Data Source: FRED"
  ) +

  annotate("point",
           x = 2019,
           y = last_val_crp,
           col = palette_prim[1],
           size=3)+
  annotate("point",
           x = 2019,
           y = last_val_mhi,
           col = palette_prim[2],
           size=3)+
  
  annotate("text",
           x = 2025,
           y = last_val_crp+.2,
           label = text_crp,
           col = palette_prim[1],
           fontface = 2,
           size = 5
  )+
  annotate("text",
           x = 2024,
           y = last_val_mhi+.39,
           label = text_mhi,
           col = palette_prim[2],
           fontface = 2,
           size = 5
  )+
  
  annotate("text",
           x = 2025,
           y = last_val_crp-.2,
           label = "\n Corporate Profits \nsince '71",
           col = palette_prim[1],
           size = 4
  )+
  annotate("text",
           x = 2024,
           y = last_val_mhi-.21,
           label = "\n Median \nHousehold income \nsince '71",
           col = palette_prim[2],
           size = 4
  )+
  annotate(
    "text",
    x = 1969,
    y = 7,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_corp_medincome,
       filename = "plots/plot_corp_medincome.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.3.3 Data

Year	Corporate Profits After Tax (billions)	Real Median Family Income
1960	280.0036	42574
1961	232.4278	43012
1962	278.0683	44229
1963	286.1980	45773
1964	340.6372	47471
1965	385.1450	49514
1966	433.1747	52129
1967	397.3241	53240
1968	408.8369	55745
1969	412.7425	58317
1970	346.8913	58137
1971	358.2173	58056
1972	419.7392	60920
1973	531.6024	62153
1974	572.9911	60493
1975	430.8478	59438
1976	565.6948	61281
1977	607.0512	61693
1978	652.0395	63616
1979	737.4178	64519
1980	680.1108	62275
1981	580.7461	60597
1982	461.1350	59814
1983	426.9457	60171
1984	511.9660	62122
1985	460.0331	63019
1986	382.4278	65783
1987	400.3520	66859
1988	511.3975	67072
1989	538.4259	68299
1990	500.2082	67258
1991	534.0683	65973
1992	563.9426	65508
1993	546.7630	64571
1994	656.5711	66370
1995	737.3910	67865
1996	805.8547	68854
1997	814.5340	71011
1998	754.7715	73472
1999	754.5291	75163
2000	740.7820	75526
2001	715.8374	74413
2002	720.1355	73647
2003	941.1858	73405
2004	1187.9377	73348
2005	1527.0718	73744
2006	1658.8931	74241
2007	1552.5998	75838
2008	1415.6915	73230
2009	1215.3858	71774
2010	1674.2083	70783
2011	1575.8833	69461
2012	1965.8843	69433
2013	1818.0484	71970
2014	1791.0288	72027
2015	1796.0716	76290
2016	1694.5861	77459
2017	1853.5374	79404
2018	1817.0092	80071
2019	1783.7735	86011

3.4 Wage Labour Hours to buy into Stock Market Indices

Without government pension programmes, US workers often rely on retirement accounts to prepare for the future. A large amount of this capital stored in retirement accounts (401k, etc.), a large portion of which is distributed into major market indices, such as the Dow Jones Industrial Average (^DJI), the S&P 500 (^GSPC), or the Russell 2000 (^RUT). But when corporate profitability is high compared to hourly wage, the average worker might have had a more difficult time hitching themselves to the profit ride that the stock market has been riding on. Here, I decided to base my analysis on how many hours it would take for a worker to buy the value of the S&P 500 - mostly because the S&P 500 and its derivative products have had a history of being a popular investment.

Admittedly the trend reversal here appears to be delayed - at least a couple of years after 1971 itself. Still, I believe this trend still fits with the overall theme: since the 70s, the average worker has had to work nearly ten times the amount of hours to buy into the most popular investment instrument.

3.4.1 Plot

3.4.2 Code

Here I call two extra packages called tidyquant and quantmod to fetch and manipulate stock market data.

library(tidyquant)
library(quantmod)

sp500 <- read_csv("data/avg_hourly_earning.csv") %>% 
  pivot_longer(cols = -Year,
               names_to = "month",
               values_to ="value") %>% 
  mutate(rtime = paste("01",month,Year,sep=" "),
         date = dmy(rtime),
         series_id = "avg_hourly_wage") %>% 
  select(date,series_id,value) %>% 
  rbind(
  fredr(
    series_id = "USACPIALLMINMEI",
    observation_start = as.Date("1960-01-01"),
    observation_end = as.Date("2019-01-02")
  )
) %>% 
  mutate(series_id = ifelse(series_id == "USACPIALLMINMEI", "cpi",series_id)) %>% 
  pivot_wider(
    id_cols = date,
    names_from = series_id,
    values_from = value
  ) %>% 
  mutate(wage = avg_hourly_wage * cpi / 100) %>% 
  select(date,wage) %>% 
  filter(!is.na(wage)) %>% 
  left_join(getSymbols("^GSPC",
           from = "1964-01-01",
           to = "2019-01-01",
           src = "yahoo",
           auto.assign=F) %>% 
  as.data.frame() %>% 
  rownames_to_column("date") %>% 
  select(date,sp500 = GSPC.Close) %>% 
  mutate(date = as.Date(date),
         year = year(date),
         month = month(date)) %>% 
  group_by(year,month) %>% 
  summarise(sp500 = first(sp500)) %>% 
  ungroup() %>% 
  mutate(date = paste(year,month,"01",sep="-") %>% ymd(),
            sp500 = sp500) %>% 
  select(date,sp500)) %>% 
  mutate(hour_to_buy = sp500/wage) 

plot_sp500 <- sp500 %>% 
  ggplot(
    aes(
      x = date,
      y = hour_to_buy
    )
  )+
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.6,
              alpha = .75)+
  geom_line(col  = palette_prim[3],
            size = 1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  labs(
    title = "Hours to buy the S&P 500 1964-2019",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "Hours\n",
    caption = "Data Source: Yahoo Finance, BLS”"
  ) + 
  theme(legend.position = "none")+
  annotate(
    "text",
    x = as.Date("1972-12-01"),
    y = 300,
    label = "1971",
    fontface = 2,
    col = palette_grid
  )

ggsave(plot = plot_sp500,
       filename = "plots/plot_sp500.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.4.3 Data

Year & Month	Avg. Nominal Hourly Wage for Nonmanagement Workers	S&P500 Nominal Value	Labour Hours to Buy S&P500
1964-1	1.048178	75.43	71.96297
1964-2	1.048178	76.97	73.43219
1964-3	1.048178	77.97	74.38622
1964-4	1.056000	79.24	75.03786
1964-5	1.056000	80.17	75.91854
1964-6	1.060726	80.11	75.52377
1964-7	1.065459	82.27	77.21551
1964-8	1.064649	83.00	77.95994
1964-9	1.073332	82.18	76.56529
1964-10	1.069396	84.08	78.62383
1964-11	1.076783	85.18	79.10597
1964-12	1.082049	83.55	77.21462
1965-1	1.082049	84.23	77.84306
1965-2	1.085998	87.58	80.64471
1965-3	1.089479	87.25	80.08416
1965-4	1.090310	86.32	79.17015
1965-5	1.098259	89.23	81.24679
1965-6	1.102588	88.72	80.46526
1965-7	1.102588	84.48	76.61977
1965-8	1.102588	85.42	77.47230
1965-9	1.110587	87.17	78.49002
1965-10	1.119451	89.90	80.30720
1965-11	1.119451	92.23	82.38858
1965-12	1.118958	91.50	81.77253
1966-1	1.128349	92.18	81.69455
1966-2	1.127345	92.16	81.74958
1966-3	1.128160	90.06	79.82913
1966-4	1.136551	89.94	79.13413
1966-5	1.140640	90.90	79.69213
1966-6	1.144171	86.10	75.25098
1966-7	1.149074	85.61	74.50349
1966-8	1.145108	82.31	71.87970
1966-9	1.161663	77.70	66.88684
1966-10	1.165992	74.90	64.23713
1966-11	1.165992	80.81	69.30577
1966-12	1.165992	80.08	68.67970
1967-1	1.174321	80.38	68.44807
1967-2	1.178485	86.43	73.33992
1967-3	1.177890	87.68	74.43818
1967-4	1.182856	89.24	75.44451
1967-5	1.186430	93.84	79.09445
1967-6	1.195623	90.23	75.46693
1967-7	1.199214	90.91	75.80802
1967-8	1.199977	95.37	79.47651
1967-9	1.212065	93.68	77.28959
1967-10	1.215672	96.32	79.23188
1967-11	1.220706	92.71	75.94787
1967-12	1.220026	94.50	77.45734
1968-1	1.241611	96.11	77.40748
1968-2	1.245253	92.56	74.33031
1968-3	1.241658	89.11	71.76696
1968-4	1.249632	92.48	74.00580
1968-5	1.259087	97.97	77.81036
1968-6	1.261994	99.99	79.23176
1968-7	1.270740	99.40	78.22214
1968-8	1.271428	97.28	76.51241
1968-9	1.292831	99.32	76.82363
1968-10	1.295730	102.86	79.38383
1968-11	1.300894	103.06	79.22244
1968-12	1.304569	108.12	82.87796
1969-1	1.309746	103.93	79.35128
1969-2	1.317104	102.89	78.11837
1969-3	1.322049	98.38	74.41481
1969-4	1.329373	101.42	76.29160
1969-5	1.339178	103.51	77.29367
1969-6	1.346536	102.94	76.44799
1969-7	1.350789	98.08	72.60941
1969-8	1.355008	93.47	68.98113
1969-9	1.375889	95.54	69.43876
1969-10	1.380158	92.52	67.03578
1969-11	1.384394	97.15	70.17509
1969-12	1.385415	93.22	67.28668
1970-1	1.393875	93.00	66.72049
1970-2	1.398043	85.75	61.33573
1970-3	1.405401	89.71	63.83230
1970-4	1.409941	90.07	63.88210
1970-5	1.418489	81.44	57.41321
1970-6	1.422565	77.84	54.71807
1970-7	1.431543	72.94	50.95202
1970-8	1.439770	77.02	53.49465
1970-9	1.457077	80.95	55.55643
1970-10	1.452875	84.32	58.03666
1970-11	1.456908	83.51	57.32002
1970-12	1.469304	87.47	59.53159
1971-1	1.482738	91.15	61.47413
1971-2	1.489830	96.42	64.71880
1971-3	1.490188	97.00	65.09244
1971-4	1.498989	100.39	66.97178
1971-5	1.511567	103.29	68.33308
1971-6	1.519393	100.20	65.94738
1971-7	1.524853	99.78	65.43583
1971-8	1.532042	95.96	62.63536
1971-9	1.549256	99.07	63.94683
1971-10	1.549602	98.93	63.84220
1971-11	1.549602	92.80	59.88635
1971-12	1.565850	95.44	60.95093
1972-1	1.595329	101.67	63.72982
1972-2	1.599607	104.01	65.02223
1972-3	1.612213	107.35	66.58547
1972-4	1.621360	107.48	66.29001
1972-5	1.625267	106.69	65.64458
1972-6	1.625655	109.69	67.47432
1972-7	1.633452	107.49	65.80540
1972-8	1.642667	108.40	65.99025
1972-9	1.662564	111.51	67.07109
1972-10	1.675817	110.16	65.73512
1972-11	1.676200	112.67	67.21750
1972-12	1.680154	117.38	69.86265
1973-1	1.687702	119.10	70.56934
1973-2	1.692347	114.76	67.81115
1973-3	1.697165	111.05	65.43264
1973-4	1.708924	110.18	64.47332
1973-5	1.722535	107.10	62.17582
1973-6	1.730576	103.93	60.05514
1973-7	1.743837	102.90	59.00781
1973-8	1.739175	106.83	61.42568
1973-9	1.773544	104.51	58.92722
1973-10	1.773847	108.21	61.00299
1973-11	1.777771	107.69	60.57585
1973-12	1.785492	93.90	52.59054
1974-1	1.789154	97.68	54.59562
1974-2	1.798251	95.32	53.00707
1974-3	1.811026	95.53	52.74910
1974-4	1.814553	93.25	51.39006
1974-5	1.845437	92.22	49.97190
1974-6	1.858559	89.10	47.94037
1974-7	1.861225	86.02	46.21687
1974-8	1.875393	78.75	41.99120
1974-9	1.912842	70.52	36.86661
1974-10	1.920964	63.39	32.99907
1974-11	1.920791	73.88	38.46333
1974-12	1.929140	68.11	35.30589
1975-1	1.938772	70.23	36.22396
1975-2	1.942582	77.82	40.06008
1975-3	1.949982	83.03	42.57987
1975-4	1.955151	82.64	42.26784
1975-5	1.963994	88.10	44.85757
1975-6	1.976500	92.58	46.84039
1975-7	1.984904	94.85	47.78568
1975-8	1.988566	87.99	44.24796
1975-9	2.022589	85.48	42.26266
1975-10	2.026753	82.93	40.91766
1975-11	2.039187	88.09	43.19859
1975-12	2.044221	90.67	44.35431
1976-1	2.057287	90.90	44.18440
1976-2	2.069396	100.87	48.74370
1976-3	2.073104	100.02	48.24648
1976-4	2.082889	102.24	49.08568
1976-5	2.102507	100.92	47.99983
1976-6	2.106482	99.85	47.40131
1976-7	2.120017	103.59	48.86282
1976-8	2.131155	103.19	48.41975
1976-9	2.170173	104.06	47.95008
1976-10	2.174148	104.17	47.91302
1976-11	2.187691	103.10	47.12731
1976-12	2.195235	102.49	46.68748
1977-1	2.209019	107.00	48.43780
1977-2	2.216714	102.54	46.25765
1977-3	2.229207	100.66	45.15507
1977-4	2.245409	99.21	44.18350
1977-5	2.261724	98.93	43.74097
1977-6	2.271605	96.93	42.67027
1977-7	2.282832	100.10	43.84904
1977-8	2.285153	99.12	43.37566
1977-9	2.321116	96.83	41.71700
1977-10	2.341672	96.74	41.31237
1977-11	2.347853	91.35	38.90789
1977-12	2.347578	94.69	40.33518
1978-1	2.381158	93.82	39.40099
1978-2	2.393744	89.93	37.56876
1978-3	2.402072	87.19	36.29782
1978-4	2.423712	88.46	36.49773
1978-5	2.435585	97.67	40.10125
1978-6	2.448263	97.35	39.76288
1978-7	2.469810	95.09	38.50093
1978-8	2.469949	100.66	40.75387
1978-9	2.519524	103.68	41.15063
1978-10	2.536594	102.96	40.58985
1978-11	2.545092	96.85	38.05364
1978-12	2.553564	96.28	37.70417
1979-1	2.579077	96.73	37.50567
1979-2	2.597624	99.96	38.48132
1979-3	2.600379	96.90	37.26380
1979-4	2.600396	100.90	38.80179
1979-5	2.627512	101.68	38.69821
1979-6	2.635558	99.17	37.62771
1979-7	2.652384	101.99	38.45221
1979-8	2.655987	104.17	39.22083
1979-9	2.713105	107.44	39.60039
1979-10	2.715890	108.56	39.97217
1979-11	2.731564	102.57	37.54992
1979-12	2.750647	105.83	38.47459
1980-1	2.767118	105.76	38.22027
1980-2	2.782940	115.12	41.36633
1980-3	2.808368	112.50	40.05885
1980-4	2.822836	102.18	36.19765
1980-5	2.830008	105.46	37.26491
1980-6	2.847188	110.76	38.90154
1980-7	2.861145	114.93	40.16923
1980-8	2.878388	121.21	42.11037
1980-9	2.920297	123.74	42.37240
1980-10	2.948109	127.13	43.12255
1980-11	2.976052	129.04	43.35945
1980-12	2.982052	137.21	46.01194
1981-1	3.024593	136.34	45.07714
1981-2	3.037339	126.91	41.78328
1981-3	3.054338	132.01	43.22050
1981-4	3.071286	136.57	44.46672
1981-5	3.087838	132.72	42.98154
1981-6	3.100056	132.41	42.71213
1981-7	3.114950	129.77	41.66039
1981-8	3.134860	130.48	41.62228
1981-9	3.177224	123.02	38.71934
1981-10	3.187982	117.08	36.72542
1981-11	3.214035	124.20	38.64301
1981-12	3.204496	126.10	39.35096
1982-1	3.254509	122.74	37.71383
1982-2	3.252889	117.78	36.20781
1982-3	3.253437	113.31	34.82778
1982-4	3.267209	113.79	34.82789
1982-5	3.290110	116.82	35.50641
1982-6	3.290397	111.68	33.94119
1982-7	3.303244	108.71	32.91007
1982-8	3.305898	108.98	32.96533
1982-9	3.341579	118.25	35.38746
1982-10	3.351819	121.97	36.38920
1982-11	3.361531	135.47	40.30009
1982-12	3.364282	138.72	41.23317
1983-1	3.408313	138.34	40.58900
1983-2	3.415928	142.96	41.85100
1983-3	3.395275	150.88	44.43822
1983-4	3.415392	153.02	44.80306
1983-5	3.431990	162.11	47.23498
1983-6	3.433973	162.55	47.33584
1983-7	3.447778	168.64	48.91266
1983-8	3.424311	162.04	47.32046
1983-9	3.488134	164.23	47.08248
1983-10	3.507048	165.81	47.27908
1983-11	3.513993	163.66	46.57380
1983-12	3.521739	166.49	47.27494
1984-1	3.564095	164.04	46.02571
1984-2	3.564301	162.74	45.65832
1984-3	3.575592	158.19	44.24163
1984-4	3.601716	157.98	43.86242
1984-5	3.586021	161.68	45.08618
1984-6	3.596426	153.24	42.60897
1984-7	3.605906	153.20	42.48586
1984-8	3.575672	154.08	43.09120
1984-9	3.628221	164.88	45.44376
1984-10	3.629702	164.62	45.35359
1984-11	3.643030	167.49	45.97547
1984-12	3.656358	162.82	44.53065
1985-1	3.672205	165.37	45.03289
1985-2	3.685137	178.63	48.47310
1985-3	3.676597	183.23	49.83684
1985-4	3.684854	181.27	49.19327
1985-5	3.680534	178.37	48.46308
1985-6	3.690824	189.32	51.29478
1985-7	3.688588	192.43	52.16902
1985-8	3.690875	192.11	52.05000
1985-9	3.746820	187.91	50.15187
1985-10	3.742314	185.07	49.45336
1985-11	3.748043	191.53	51.10133
1985-12	3.772194	200.46	53.14150
1986-1	3.768675	209.59	55.61371
1986-2	3.786028	213.96	56.51305
1986-3	3.787070	225.42	59.52359
1986-4	3.784690	235.14	62.12925
1986-5	3.781362	235.16	62.18924
1986-6	3.783716	245.04	64.76173
1986-7	3.774476	252.04	66.77483
1986-8	3.776742	234.91	62.19912
1986-9	3.821852	248.52	65.02606
1986-10	3.829974	233.60	60.99258
1986-11	3.852078	245.80	63.80971
1986-12	3.846243	249.05	64.75150
1987-1	3.865917	246.45	63.74943
1987-2	3.870406	276.45	71.42662
1987-3	3.868828	283.00	73.14877
1987-4	3.870516	292.38	75.54032
1987-5	3.879481	288.03	74.24446
1987-6	3.864470	289.83	74.99865
1987-7	3.860280	302.94	78.47617
1987-8	3.875806	317.57	81.93650
1987-9	3.925246	323.40	82.38974
1987-10	3.940350	327.33	83.07129
1987-11	3.958374	255.75	64.60986
1987-12	3.958374	232.00	58.60992
1988-1	3.978428	255.94	64.33195
1988-2	3.974061	255.04	64.17617
1988-3	3.976445	267.22	67.20073
1988-4	4.001865	256.09	63.99266
1988-5	4.010577	261.56	65.21754
1988-6	3.997772	266.69	66.70965
1988-7	4.004713	271.78	67.86504
1988-8	4.001527	272.21	68.02652
1988-9	4.068864	258.35	63.49438
1988-10	4.092593	271.38	66.31005
1988-11	4.095997	279.06	68.12993
1988-12	4.097723	272.49	66.49790
1989-1	4.138564	275.31	66.52308
1989-2	4.140260	297.09	71.75636
1989-3	4.148614	287.11	69.20625
1989-4	4.160170	296.39	71.24469
1989-5	4.147264	309.12	74.53589
1989-6	4.146842	321.97	77.64222
1989-7	4.167364	319.23	76.60239
1989-8	4.158292	343.75	82.66614
1989-9	4.229654	353.73	83.63095
1989-10	4.244658	350.87	82.66156
1989-11	4.254796	341.20	80.19186
1989-12	4.266875	350.63	82.17489
1990-1	4.283988	359.69	83.96149
1990-2	4.304164	328.79	76.38882
1990-3	4.316842	332.74	77.07949
1990-4	4.334428	338.70	78.14181
1990-5	4.339064	332.25	76.57181
1990-6	4.340651	363.16	83.66488
1990-7	4.357358	359.54	82.51330
1990-8	4.353038	355.52	81.67169
1990-9	4.411819	323.09	73.23284
1990-10	4.404621	314.94	71.50218
1990-11	4.408874	307.02	69.63683
1990-12	4.425809	324.10	73.22955
1991-1	4.446592	326.45	73.41577
1991-2	4.453200	343.05	77.03450
1991-3	4.454111	370.47	83.17485
1991-4	4.477822	371.30	82.91977
1991-5	4.479628	380.29	84.89321
1991-6	4.487104	388.06	86.48339
1991-7	4.487957	377.92	84.20759
1991-8	4.489610	387.12	86.22574
1991-9	4.549851	392.15	86.18964
1991-10	4.544889	389.20	85.63465
1991-11	4.546492	391.32	86.07075
1991-12	4.567246	381.40	83.50765
1992-1	4.573870	417.26	91.22690
1992-2	4.584582	409.53	89.32765
1992-3	4.595983	412.45	89.74142
1992-4	4.596696	404.23	87.93926
1992-5	4.597392	412.53	89.73131
1992-6	4.590185	417.30	90.91136
1992-7	4.594080	412.88	89.87219
1992-8	4.601214	425.09	92.38648
1992-9	4.650046	416.07	89.47653
1992-10	4.660518	416.29	89.32269
1992-11	4.673082	422.75	90.46491
1992-12	4.669792	430.78	92.24823
1993-1	4.704861	435.38	92.53834
1993-2	4.703245	442.52	94.08823
1993-3	4.713620	442.01	93.77295
1993-4	4.720674	450.30	95.38892
1993-5	4.727231	442.46	93.59814
1993-6	4.709417	453.83	96.36648
1993-7	4.709417	449.02	95.34512
1993-8	4.722463	450.15	95.32102
1993-9	4.775100	463.15	96.99272
1993-10	4.782552	461.28	96.45061
1993-11	4.791985	469.10	97.89262
1993-12	4.804288	461.89	96.14119
1994-1	4.842142	465.44	96.12275
1994-2	4.846323	479.62	98.96575
1994-3	4.837999	464.44	95.99837
1994-4	4.850791	438.92	90.48421
1994-5	4.860305	453.02	93.20813
1994-6	4.833071	457.63	94.68721
1994-7	4.839872	446.20	92.19252
1994-8	4.834295	461.01	95.36242
1994-9	4.897699	473.17	96.61067
1994-10	4.919900	461.74	93.85149
1994-11	4.920166	468.42	95.20411
1994-12	4.932798	448.92	91.00717
1995-1	4.958910	459.11	92.58284
1995-2	4.959606	470.40	94.84624
1995-3	4.963264	485.65	97.84891
1995-4	4.979655	501.85	100.78007
1995-5	4.970226	514.26	103.46814
1995-6	4.960720	533.49	107.54285
1995-7	4.986457	547.09	109.71519
1995-8	4.980183	559.64	112.37339
1995-9	5.048127	563.84	111.69291
1995-10	5.064603	581.72	114.85994
1995-11	5.061308	584.22	115.42866
1995-12	5.070965	606.98	119.69713
1996-1	5.113726	620.73	121.38507
1996-2	5.110680	638.46	124.92663
1996-3	5.110798	644.37	126.08012
1996-4	5.137087	653.73	127.25694
1996-5	5.127126	654.58	127.66997
1996-6	5.143623	667.68	129.80734
1996-7	5.140222	675.88	131.48849
1996-8	5.156681	650.02	126.05396
1996-9	5.232992	654.72	125.11389
1996-10	5.229536	689.08	131.76694
1996-11	5.246138	703.77	134.15010
1996-12	5.272905	756.56	143.48069
1997-1	5.302953	737.01	138.98106
1997-2	5.306151	786.73	148.26754
1997-3	5.326200	795.31	149.32033
1997-4	5.332858	759.64	142.44520
1997-5	5.329529	798.53	149.83125
1997-6	5.336187	846.36	158.60764
1997-7	5.336073	891.03	166.98236
1997-8	5.366400	947.14	176.49449
1997-9	5.427358	927.58	170.90822
1997-10	5.454461	955.41	175.16120
1997-11	5.485155	938.99	171.18750
1997-12	5.491973	974.77	177.48995
1998-1	5.529460	975.04	176.33548
1998-2	5.553387	1001.27	180.29900
1998-3	5.563677	1047.70	188.31071
1998-4	5.573967	1108.15	198.80813
1998-5	5.570520	1121.00	201.23794
1998-6	5.556732	1090.98	196.33481
1998-7	5.563550	1148.56	206.44373
1998-8	5.604838	1112.44	198.47850
1998-9	5.660016	994.26	175.66382
1998-10	5.666935	986.39	174.06058
1998-11	5.694613	1111.60	195.20204
1998-12	5.691140	1175.28	206.51046
1999-1	5.739690	1228.10	213.96627
1999-2	5.739736	1273.00	221.78721
1999-3	5.750220	1236.16	214.97611
1999-4	5.778016	1293.72	223.90384
1999-5	5.792040	1354.63	233.87787
1999-6	5.763992	1294.26	224.54231
1999-7	5.781332	1380.96	238.86536
1999-8	5.795205	1328.05	229.16362
1999-9	5.865453	1331.07	226.93388
1999-10	5.875933	1282.81	218.31598
1999-11	5.879426	1354.12	230.31498
1999-12	5.900729	1397.72	236.87244
2000-1	5.946746	1455.22	244.70860
2000-2	5.946156	1409.28	237.00691
2000-3	5.951843	1379.19	231.72485
2000-4	6.005911	1505.97	250.74797
2000-5	5.983980	1468.25	245.36345
2000-6	5.979014	1448.81	242.31588
2000-7	6.014758	1469.54	244.32237
2000-8	6.014758	1438.10	239.09522
2000-9	6.090057	1520.77	249.71360
2000-10	6.122599	1436.23	234.57849
2000-11	6.126118	1421.22	231.99359
2000-12	6.151964	1315.23	213.79027
2001-1	6.176080	1283.27	207.78066
2001-2	6.193353	1373.47	221.76516
2001-3	6.207445	1241.23	199.95827
2001-4	6.239569	1145.87	183.64569
2001-5	6.222803	1266.44	203.51601
2001-6	6.218288	1260.67	202.73587
2001-7	6.245755	1236.72	198.00970
2001-8	6.245755	1215.93	194.68105
2001-9	6.319040	1132.94	179.28987
2001-10	6.320268	1038.55	164.32057
2001-11	6.347022	1084.10	170.80452
2001-12	6.381618	1129.90	177.05541
2002-1	6.396064	1154.67	180.52821
2002-2	6.398841	1122.20	175.37551
2002-3	6.397111	1131.78	176.92050
2002-4	6.410131	1146.54	178.86375
2002-5	6.402545	1086.46	169.69189
2002-6	6.428876	1040.68	161.87588
2002-7	6.413228	968.65	151.03939
2002-8	6.442217	884.66	137.32228
2002-9	6.521642	878.02	134.63174
2002-10	6.524802	847.91	129.95183
2002-11	6.540101	900.96	137.75935
2002-12	6.579098	934.53	142.04532
2003-1	6.585194	909.03	138.04149
2003-2	6.620483	860.32	129.94823
2003-3	6.605855	834.81	126.37425
2003-4	6.591510	858.48	130.24025
2003-5	6.596236	916.30	138.91256
2003-6	6.611176	967.00	146.26748
2003-7	6.610614	982.32	148.59738
2003-8	6.620200	980.15	148.05444
2003-9	6.672973	1021.99	153.15363
2003-10	6.665767	1018.22	152.75362
2003-11	6.710025	1059.02	157.82654
2003-12	6.694975	1070.12	159.83927
2004-1	6.727669	1108.48	164.76434
2004-2	6.740428	1135.26	168.42551
2004-3	6.728522	1155.97	171.80148
2004-4	6.742132	1132.17	167.92462
2004-5	6.757646	1117.49	165.36675
2004-6	6.723062	1121.20	166.76924
2004-7	6.744394	1128.94	167.38939
2004-8	6.771940	1106.62	163.41254
2004-9	6.826295	1105.91	162.00736
2004-10	6.838079	1131.50	165.47045
2004-11	6.841661	1130.51	165.23911
2004-12	6.856732	1191.37	173.75187
2005-1	6.911373	1202.08	173.92780
2005-2	6.894594	1189.41	172.51342
2005-3	6.907737	1210.41	175.22527
2005-4	6.913141	1172.92	169.66528
2005-5	6.930642	1162.16	167.68432
2005-6	6.901383	1202.22	174.19987
2005-7	6.933317	1194.44	172.27540
2005-8	6.935654	1235.35	178.11585
2005-9	6.978470	1221.59	175.05126
2005-10	7.042938	1226.70	174.17447
2005-11	7.028053	1202.76	171.13702
2005-12	7.057722	1264.67	179.18956
2006-1	7.128248	1268.80	177.99605
2006-2	7.134244	1282.46	179.76117
2006-3	7.140020	1291.24	180.84544
2006-4	7.200770	1297.81	180.23211
2006-5	7.159613	1305.19	182.29895
2006-6	7.165195	1285.71	179.43824
2006-7	7.212141	1280.19	177.50484
2006-8	7.209112	1270.92	176.29357
2006-9	7.293603	1311.01	179.74792
2006-10	7.347718	1331.32	181.18823
2006-11	7.328293	1367.81	186.64784
2006-12	7.364746	1396.71	189.64808
2007-1	7.412847	1416.60	191.10065
2007-2	7.435337	1445.94	194.46864
2007-3	7.442393	1403.17	188.53748
2007-4	7.482020	1424.55	190.39646
2007-5	7.448778	1486.30	199.53609
2007-6	7.463214	1536.34	205.85502
2007-7	7.522834	1519.43	201.97576
2007-8	7.517810	1465.81	194.97834
2007-9	7.608899	1489.42	195.74711
2007-10	7.589915	1547.04	203.82837
2007-11	7.590659	1508.44	198.72320
2007-12	7.638736	1472.42	192.75702
2008-1	7.658894	1447.16	188.95158
2008-2	7.690067	1395.42	181.45745
2008-3	7.729700	1331.34	172.23696
2008-4	7.713133	1370.18	177.64247
2008-5	7.695825	1409.34	183.13045
2008-6	7.708752	1385.67	179.75284
2008-7	7.721389	1284.91	166.40918
2008-8	7.764517	1260.31	162.31662
2008-9	7.827623	1277.58	163.21428
2008-10	7.858203	1161.06	147.75134
2008-11	7.940719	966.30	121.68922
2008-12	7.956160	816.21	102.58844
2009-1	7.990788	931.80	116.60927
2009-2	8.030526	825.44	102.78779
2009-3	8.050054	700.82	87.05780
2009-4	8.016169	811.08	101.18050
2009-5	7.985189	877.52	109.89345
2009-6	7.953678	942.87	118.54516
2009-7	7.977410	923.33	115.74308
2009-8	8.022621	1002.63	124.97537
2009-9	8.064087	998.04	123.76355
2009-10	8.080974	1029.85	127.44132
2009-11	8.123202	1042.88	128.38287
2009-12	8.099784	1108.86	136.89994
2010-1	8.154892	1132.99	138.93378
2010-2	8.166069	1089.19	133.37996
2010-3	8.135326	1115.71	137.14385
2010-4	8.158655	1178.10	144.39881
2010-5	8.183390	1202.26	146.91468
2010-6	8.120223	1070.71	131.85721
2010-7	8.140333	1027.37	126.20736
2010-8	8.188415	1125.86	137.49424
2010-9	8.220828	1080.29	131.40892
2010-10	8.258747	1146.24	138.79103
2010-11	8.262221	1184.38	143.34886
2010-12	8.248677	1206.07	146.21374
2011-1	8.362299	1271.87	152.09573
2011-2	8.310164	1307.59	157.34828
2011-3	8.278057	1306.33	157.80635
2011-4	8.293407	1332.41	160.65895
2011-5	8.303820	1361.22	163.92697
2011-6	8.247309	1314.55	159.39138
2011-7	8.292744	1339.67	161.54725
2011-8	8.277380	1286.94	155.47673
2011-9	8.347385	1204.42	144.28711
2011-10	8.416143	1099.23	130.60971
2011-11	8.370864	1218.28	145.53814
2011-12	8.378781	1244.58	148.53950
2012-1	8.463466	1277.06	150.89091
2012-2	8.414282	1324.09	157.36220
2012-3	8.400758	1374.09	163.56737
2012-4	8.464968	1419.04	167.63680
2012-5	8.396857	1405.82	167.42216
2012-6	8.384544	1278.04	152.42809
2012-7	8.448208	1365.51	161.63309
2012-8	8.388305	1375.32	163.95685
2012-9	8.494078	1404.94	165.40229
2012-10	8.481015	1444.49	170.32041
2012-11	8.499111	1427.59	167.96933
2012-12	8.553719	1409.46	164.77744
2013-1	8.598446	1462.42	170.07956
2013-2	8.600301	1513.17	175.94385
2013-3	8.573682	1518.20	177.07678
2013-4	8.604011	1562.17	181.56300
2013-5	8.560360	1582.70	184.88707
2013-6	8.590755	1640.42	190.95180
2013-7	8.584284	1614.96	188.12985
2013-8	8.574875	1706.87	199.05480
2013-9	8.703395	1639.77	188.40578
2013-10	8.680982	1695.00	195.25441
2013-11	8.712419	1761.64	202.19873
2013-12	8.751001	1800.90	205.79359
2014-1	8.793427	1831.98	208.33517
2014-2	8.865566	1741.89	196.47814
2014-3	8.832936	1845.73	208.95996
2014-4	8.812044	1885.52	213.97078
2014-5	8.772561	1883.68	214.72409
2014-6	8.798952	1924.97	218.77264
2014-7	8.795519	1973.32	224.35515
2014-8	8.790861	1925.15	218.99448
2014-9	8.857734	2002.28	226.04880
2014-10	8.895586	1946.16	218.77818
2014-11	8.967116	2017.81	225.02330
2014-12	8.906365	2053.44	230.55870
2015-1	9.032078	2058.20	227.87667
2015-2	9.061402	2020.85	223.01736
2015-3	9.065522	2117.39	233.56513
2015-4	9.034040	2059.69	227.99213
2015-5	9.019889	2108.29	233.73792
2015-6	8.981005	2111.73	235.13291
2015-7	9.011815	2077.42	230.52182
2015-8	9.059380	2098.04	231.58759
2015-9	9.095473	1913.85	210.41786
2015-10	9.141556	1923.82	210.44776
2015-11	9.192355	2104.05	228.89129
2015-12	9.160944	2102.63	229.52109
2016-1	9.266050	2012.66	217.20799
2016-2	9.273676	1939.38	209.12742
2016-3	9.263374	1978.35	213.56690
2016-4	9.297197	2072.78	222.94676
2016-5	9.304405	2081.43	223.70371
2016-6	9.233276	2099.33	227.36567
2016-7	9.289402	2102.95	226.38163
2016-8	9.277610	2170.84	233.98699
2016-9	9.361030	2170.86	231.90397
2016-10	9.433894	2161.20	229.08885
2016-11	9.388671	2111.72	224.92214
2016-12	9.391744	2191.08	233.29853
2017-1	9.518196	2257.83	237.21197
2017-2	9.486474	2279.55	240.29476
2017-3	9.473612	2395.96	252.90880
2017-4	9.553290	2358.84	246.91389
2017-5	9.478851	2388.33	251.96408
2017-6	9.456445	2430.06	256.97395
2017-7	9.542871	2429.01	254.53661
2017-8	9.498936	2476.35	260.69764
2017-9	9.590886	2476.55	258.21911
2017-10	9.636859	2529.12	262.44237
2017-11	9.595464	2579.36	268.81035
2017-12	9.621027	2642.22	274.62972
2018-1	9.704813	2695.81	277.78072
2018-2	9.706801	2821.98	290.72194
2018-3	9.728749	2677.67	275.23270
2018-4	9.820276	2581.88	262.91317
2018-5	9.744355	2654.80	272.44492
2018-6	9.717360	2734.62	281.41596
2018-7	9.792442	2726.71	278.45045
2018-8	9.776606	2813.36	287.76451
2018-9	9.926425	2896.72	291.81906
2018-10	9.890615	2924.59	295.69344
2018-11	9.931924	2740.37	275.91532
2018-12	10.037999	2790.37	277.98070
2019-1	10.057138	NA	NA

3.5 CEO-to-worker Compensation Ratio

And this precipitous increase in corporate profits and valuation, having not trickled down to the middle class, appears to have mostly gone to the corporate executives of America. While in the 60s and early 70s, a CEO only earned about 20x to 25x the amount their worker did, this ratio skyrocketed to more 320 by 2019. This change may not have taken place dramatically in the year 1971, but it can be argued that the decade following ’71 was a long process of trend reversal from relative parity to the outsized ratio of modern America.

3.5.1 Plot

3.5.2 Code

plot_ceo_ratio <- read_csv("data/ceo_comp.csv") %>% 
  mutate(
    realized = realized %>% str_extract("^\\d+.{0,1}\\d+") %>% as.numeric()
  ) %>% 
  ggplot(
    aes(
      x = year,
      y = realized
    )
  )+
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.6,
              alpha = .75)+
  geom_line(col  = palette_prim[3],
            size = 1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  labs(
    title = "CEO-worker compensation ratio 1965-2019",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "Ratio\n",
    caption = "Data Source: Economic Policy Institute”"
  ) +
  annotate(
    "text",
    x = 1969,
    y = 350,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  theme(legend.position = "none")

ggsave(plot = plot_ceo_ratio,
       filename = "plots/plot_ceo_ratio.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.5.3 Data

Year	Realized Compensation Ratio
1965	21.1
1966	22.3
1967	23.6
1968	24.8
1969	24.6
1970	24.3
1971	24.0
1972	23.7
1973	23.4
1974	25.0
1975	26.6
1976	28.2
1977	29.8
1978	31.4
1979	34.1
1980	36.9
1981	39.6
1982	42.3
1983	45.0
1984	47.8
1985	50.5
1986	53.2
1987	55.9
1988	58.7
1989	61.4
1990	77.3
1991	93.1
1992	109.0
1993	108.6
1994	87.4
1995	117.6
1996	150.6
1997	223.4
1998	297.4
1999	266.1
2000	365.7
2001	210.6
2002	186.8
2003	228.8
2004	265.7
2005	318.4
2006	328.2
2007	330.9
2008	206.7
2009	177.6
2010	213.1
2011	242.4
2012	371.7
2013	318.5
2014	326.6
2015	318.8
2016	271.6
2017	302.1
2018	293.3
2019	320.0

3.6 Inequality 1: Gini Index

Unsurprisingly this previous pattern has corresponded with an increase in the Gini Index, measuring household income inequality. Here it is clear through the smoothed trendline that the years around 1971 were the bottom of the valley that signalled a trend reversal towards the massive inequality in modern America.

Definition of the Gini Index, from the OECD: The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. The Gini index measures the area between the Lorenz curve and the hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Gini index of zero represents perfect equality and 1.00, perfect inequality.

3.6.1 Plot

3.6.2 Code

gini <- fredr(
  series_id = "GINIALLRF",
  observation_start = as.Date("1800-01-01"),
  observation_end = as.Date(Sys.Date())
) 

plot_gini <- gini %>% 
  mutate(date = year(date)) %>% 
  ggplot(
    aes(
      x = date,
      y = value
    )
  ) + 
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.25,
              alpha = .75)+
  geom_line(col  = palette_prim[3],
            size = 1.25)+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  labs(
    title = "Gini Index of Household Income Inequality 1947-2018",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "Gini Coefficient \n",
    caption = "Data Source: FRED"
  ) +
  annotate(
    "text",
    x = 1969,
    y = 0.46,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  
  theme(legend.position = "none")

ggsave(plot = plot_gini,
       filename = "plots/plot_gini.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.6.3 Data

Year	Gini Coeffcient
1947	0.376
1948	0.371
1949	0.378
1950	0.379
1951	0.363
1952	0.368
1953	0.359
1954	0.371
1955	0.363
1956	0.358
1957	0.351
1958	0.354
1959	0.361
1960	0.364
1961	0.374
1962	0.362
1963	0.362
1964	0.361
1965	0.356
1966	0.349
1967	0.358
1968	0.348
1969	0.349
1970	0.353
1971	0.355
1972	0.359
1973	0.356
1974	0.355
1975	0.357
1976	0.358
1977	0.363
1978	0.363
1979	0.365
1980	0.365
1981	0.369
1982	0.380
1983	0.382
1984	0.383
1985	0.389
1986	0.392
1987	0.393
1988	0.395
1989	0.401
1990	0.396
1991	0.397
1992	0.404
1993	0.429
1994	0.426
1995	0.421
1996	0.425
1997	0.429
1998	0.430
1999	0.429
2000	0.433
2001	0.435
2002	0.434
2003	0.436
2004	0.438
2005	0.440
2006	0.444
2007	0.432
2008	0.438
2009	0.443
2010	0.440
2011	0.450
2012	0.451
2013	0.455
2014	0.452
2015	0.448
2016	0.452
2017	0.458
2018	0.452
2019	0.454

3.7 Inequality 2: Top 10% as share of total income

In a similar pattern, while in the post-war era, the top 10% of earners in America accounted for an unprecedentedly small proportion of total income, this proportion started to increase dramatically again, with the trend reversal beginning around 1971. Data here is taken from Saez and Piketty’s original research.

3.7.1 Plot

3.7.2 Code

plot_top10_incomeshare <- read_csv("data/fct_income_distro.csv") %>% 
  select(year,top10) %>% 
  pivot_longer(
    cols = -year,
    names_to = "pop",
    values_to = "share"
  ) %>% 
  ggplot(
    aes(
      x = year,
      y = share,
      col = pop
    )
  )+
  geom_smooth(col   = palette_prim[2],
              fill  = palette_2nd[2],
              size  = 1.6,
              alpha = .75)+
  geom_line(size = 1.25,
            col = palette_prim[3])+
  geom_vline(
    xintercept = 1971,
    linetype = "dashed",
    size = 0.75,
    col = palette_grid
  )+
  theme_pelican70()+
  scale_x_continuous(breaks = seq(1900,2020,20))+
  scale_y_continuous(labels = scales::percent)+
  labs(
    title = "Share of total income by top 10% earners 1917-1998",
    subtitle = "(with Loess smoothed trendline)",
    x = "Year",
    y = "% Share of total \n",
    caption = "Data Source: Piketty and Saez, 2003”"
  ) +
  annotate(
    "text",
    x = 1968,
    y = 0.49,
    label = "1971",
    fontface = 2,
    col = palette_grid
  ) +
  theme(legend.position = "none")

ggsave(plot = plot_top10_incomeshare,
       filename = "plots/plot_top10_incomeshare.png",
       h = 5,
       w = 10,
       type = "cairo-png")

3.7.3 Data

Year	Top 10%	Top 5%	Top 1%	Top 0.5%	Top 0.1%	Top 0.01%
1913	0.4333371	0.3305022	0.2053119	0.1744347	0.0923486	0.0317253
1914	0.4394441	0.3373522	0.2102100	0.1796748	0.0936562	0.0346043
1915	0.4321828	0.3298351	0.2037836	0.1744433	0.0968058	0.0428369
1916	0.4460024	0.3499577	0.2145887	0.1806900	0.1140153	0.0495340
1917	0.4517593	0.3597944	0.2140942	0.1746333	0.1026482	0.0395196
1918	0.4424876	0.3462763	0.1979529	0.1552008	0.0841323	0.0288916
1919	0.4617415	0.3712198	0.2172954	0.1692353	0.0893450	0.0289547
1920	0.4439024	0.3453164	0.1922266	0.1449608	0.0690709	0.0190420
1921	0.4756666	0.3604981	0.1927004	0.1455222	0.0695543	0.0191110
1922	0.4630841	0.3509109	0.1867308	0.1414222	0.0688050	0.0210190
1923	0.4393464	0.3323454	0.1779217	0.1353732	0.0657626	0.0197157
1924	0.4594547	0.3459150	0.1856044	0.1414393	0.0689394	0.0207939
1925	0.4745530	0.3692036	0.2076695	0.1591498	0.0805440	0.0274063
1926	0.4783624	0.3781228	0.2198594	0.1699015	0.0895978	0.0321015
1927	0.4732464	0.3721427	0.2131644	0.1640334	0.0859137	0.0308086
1928	0.4844467	0.3821951	0.2236085	0.1742246	0.0956928	0.0370300
1929	0.4716386	0.3758773	0.2224790	0.1744123	0.0987616	0.0405552
1930	0.4654305	0.3554737	0.1943886	0.1470222	0.0753468	0.0266947
1931	0.4660781	0.3391211	0.1681897	0.1230676	0.0583588	0.0185423
1932	0.4858131	0.3544893	0.1655634	0.1229813	0.0585266	0.0149866
1933	0.4827451	0.3624026	0.1751608	0.1312048	0.0646577	0.0185968
1934	0.4932814	0.3814154	0.1911131	0.1455317	0.0713578	0.0213557
1935	0.4831617	0.3679803	0.1938906	0.1483617	0.0737502	0.0226430
1936	0.4860467	0.3731407	0.2097519	0.1615526	0.0792884	0.0230865
1937	0.4772440	0.3650566	0.2070979	0.1588284	0.0785776	0.0237167
1938	0.4733016	0.3508255	0.1868631	0.1404268	0.0688876	0.0234690
1939	0.4882407	0.3634298	0.1960684	0.1475756	0.0713397	0.0215798
1940	0.4911092	0.3713569	0.2089458	0.1594255	0.0796649	0.0260271
1941	0.4715121	0.3661888	0.2159736	0.1652387	0.0839767	0.0283625
1942	0.4254670	0.3368301	0.2062793	0.1593192	0.0803333	0.0252391
1943	0.3916021	0.3109285	0.1865804	0.1410814	0.0682211	0.0185650
1944	0.3631141	0.2755470	0.1541485	0.1139722	0.0533749	0.0163133
1945	0.3557713	0.2686349	0.1432875	0.1031265	0.0455017	0.0121387
1946	0.3722210	0.2823528	0.1432552	0.1009792	0.0431305	0.0125581
1947	0.3713386	0.2858498	0.1502405	0.1080299	0.0496726	0.0161372
1948	0.3906762	0.3016048	0.1635261	0.1202975	0.0568377	0.0180546
1949	0.3853625	0.2924830	0.1573189	0.1159369	0.0552045	0.0179624
1950	0.3922462	0.3009263	0.1674159	0.1229772	0.0595827	0.0151546
1951	0.3811005	0.2920847	0.1604361	0.1170173	0.0550234	0.0178189
1952	0.3685850	0.2786406	0.1499114	0.1091833	0.0513007	0.0162292
1953	0.3588413	0.2674690	0.1405794	0.1020816	0.0475397	0.0155246
1954	0.3615750	0.2687355	0.1393599	0.0999914	0.0466189	0.0145659
1955	0.3687712	0.2750502	0.1461809	0.1056338	0.0515806	0.0172474
1956	0.3586891	0.2654753	0.1379306	0.1016344	0.0480587	0.0154452
1957	0.3582972	0.2652427	0.1349883	0.0986901	0.0458349	0.0141702
1958	0.3564746	0.2592521	0.1261834	0.0907839	0.0407342	0.0123379
1959	0.3607414	0.2645023	0.1314602	0.0957372	0.0433264	0.0132543
1960	0.3562313	0.2574691	0.1261439	0.0911476	0.0427853	0.0145803
1961	0.3569859	0.2573828	0.1244148	0.0894055	0.0424586	0.0147527
1962	0.3613481	0.2604818	0.1261647	0.0911020	0.0426696	0.0144227
1963	0.3624446	0.2615898	0.1266419	0.0917370	0.0434188	0.0149748
1964	0.3635411	0.2626977	0.1271192	0.0923720	0.0441680	0.0155269
1965	0.3599449	0.2601079	0.1263451	0.0921294	0.0444865	0.0157032
1966	0.3563487	0.2575181	0.1255709	0.0918868	0.0448050	0.0158796
1967	0.3522355	0.2536897	0.1226331	0.0891639	0.0424676	0.0145031
1968	0.3488327	0.2501628	0.1201499	0.0871808	0.0413171	0.0140288
1969	0.3399681	0.2404240	0.1125239	0.0809116	0.0377438	0.0129499
1970	0.3370734	0.2357901	0.1073542	0.0762765	0.0347541	0.0117322
1971	0.3403034	0.2380933	0.1081123	0.0767928	0.0349805	0.0117191
1972	0.3424716	0.2395630	0.1079242	0.0763070	0.0345885	0.0114937
1973	0.3419868	0.2388404	0.1059334	0.0742853	0.0332759	0.0107693
1974	0.3389170	0.2349509	0.1032441	0.0722258	0.0321063	0.0103596
1975	0.3406429	0.2351802	0.1030850	0.0719333	0.0320173	0.0105714
1976	0.3412797	0.2354719	0.1030641	0.0720724	0.0322211	0.0107325
1977	0.3419326	0.2361789	0.1034741	0.0725494	0.0325412	0.0108807
1978	0.3409258	0.2354379	0.1034481	0.0729761	0.0332592	0.0113280
1979	0.3425164	0.2380716	0.1067121	0.0762357	0.0360266	0.0128225
1980	0.3401733	0.2337849	0.1027903	0.0728970	0.0337117	0.0115469
1981	0.3441539	0.2368951	0.1049075	0.0747511	0.0349213	0.0120825
1982	0.3485344	0.2397379	0.1076202	0.0779525	0.0388906	0.0152782
1983	0.3545140	0.2441762	0.1110138	0.0809154	0.0409097	0.0172215
1984	0.3596841	0.2501457	0.1154284	0.0851024	0.0436300	0.0180630
1985	0.3611107	0.2512243	0.1173539	0.0867153	0.0448796	0.0183161
1986	0.3597864	0.2485901	0.1137119	0.0832807	0.0414024	0.0168528
1987	0.3700553	0.2606897	0.1246581	0.0925761	0.0476849	0.0204081
1988	0.3856257	0.2778675	0.1413977	0.1078970	0.0579889	0.0241027
1989	0.3820587	0.2738112	0.1370576	0.1035599	0.0545000	0.0228601
1990	0.3832241	0.2742996	0.1376733	0.1038890	0.0543063	0.0226277
1991	0.3829378	0.2717450	0.1316371	0.0980381	0.0503419	0.0207310
1992	0.3944372	0.2830691	0.1413139	0.1064086	0.0563515	0.0236993
1993	0.3915791	0.2793503	0.1361625	0.1016088	0.0531196	0.0221129
1994	0.3906146	0.2781474	0.1347971	0.1002252	0.0519671	0.0214973
1995	0.3979319	0.2850787	0.1402758	0.1050196	0.0550491	0.0227894
1996	0.4061743	0.2944113	0.1475645	0.1113264	0.0597775	0.0255625
1997	0.4128095	0.3017584	0.1544617	0.1177506	0.0642970	0.0277916
1998	0.4166779	0.3059773	0.1586918	0.1216574	0.0671484	0.0286588
1999	0.4206951	0.3098797	0.1623893	0.1252473	0.0701270	0.0304722
2000	0.4265723	0.3162443	0.1695275	0.1320947	0.0758194	0.0338332
2001	0.4202892	0.3090904	0.1624824	0.1255752	0.0703299	0.0304399
2002	0.4170105	0.3048225	0.1579016	0.1210236	0.0671813	0.0294677
2003	0.4192948	0.3067952	0.1599950	0.1232462	0.0693175	0.0312881
2004	0.4251735	0.3136402	0.1666804	0.1292530	0.0732875	0.0325762
2005	0.4342811	0.3233857	0.1748806	0.1366006	0.0785479	0.0343092
2006	0.4421310	0.3308800	0.1810527	0.1421320	0.0820945	0.0358069
2007	0.4421775	0.3316491	0.1827772	0.1440801	0.0842070	0.0383803
2008	0.4417405	0.3290365	0.1787328	0.1405258	0.0826122	0.0385976
2009	0.4358040	0.3189771	0.1668946	0.1294684	0.0740768	0.0333726
2010	0.4462403	0.3305499	0.1769001	0.1381596	0.0795252	0.0348214
2011	0.4528851	0.3373202	0.1823434	0.1428733	0.0829748	0.0379600
2012	0.4624456	0.3480668	0.1927508	0.1521953	0.0894757	0.0408309
2013	0.4563442	0.3403918	0.1834086	0.1432901	0.0835862	0.0380791
2014	0.4613220	0.3458798	0.1883998	0.1474539	0.0857489	0.0390228
2015	0.4629940	0.3467692	0.1881906	0.1473641	0.0854856	0.0387437
2016	0.4617064	0.3445860	0.1858382	0.1450613	0.0838123	0.0373327
2017	0.4590072	0.3439488	0.1865074	0.1462892	0.0855982	0.0402410
2018	0.4594392	0.3448937	0.1877627	0.1471784	0.0860407	0.0401177
2019	0.4584145	0.3434592	0.1860017	0.1453777	0.0842893	0.0383910